Automatic generation of virtual assistants from databases using active ontologies

Abstract

Virtual assistants such as Siri or Google Assistant are omnipresent. However, their development remains costly. One must either manually model the problem domain or provide thousands of labeled samples. We propose to automatically create virtual assistants based on Active Ontologies for interacting with databases. Our approach generates Active Ontologies; we use the database structure to derive a concept hierarchy and database values together with synonyms to extract information from user queries. Our approach also learns common phrases from samples, e.g. from existing Dialogflow agents. We extract pre- and postfixes and attach them to concepts, e.g. at to detect a succeeding location. The generated Active Ontologies reply to previously unseen and composed requests. The approach is not limited to virtual assistants but can be applied to any system with a textual or voice-based conversational interface such as chatbots. We evaluate our approach in three domains: tourism, hotel, and web cams. The study shows that automatically generated Active Ontologies extract relevant information from user utterances with a precision of 58%. The precision increases to 79% (recall 46%, F1 58%) when we use sample utterances. Our approach successfully transfers between domains, e.g. we learn phrases from the tourism domain and use them to reply to hotel requests without any adjustments.